Natural Launguage Processing ,Text-Mining ,Natural Launguage Understanding
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Updated
May 26, 2020 - Jupyter Notebook
Natural Launguage Processing ,Text-Mining ,Natural Launguage Understanding
Hugging Face Transformers, a popular Python library, offers pre-trained models for various powerful toolkit for NLP tasks, opening doors to career opportunities and be part of the innovation that will change the world with shaping the future of human-machine interaction.
Natural Language Processing Practicals on different concepts to analyze and understand the practical implementation their use and actual use.
Fine-tuned NER models for banking and regulation texts, trained on eCFR Title 12 using manual and few-shot (GPT 3.5 v3) annotations.
Custom name entity model with spaCy for specific domains
Text Annotation App
An unsupervised model to clustering Thai news. Using TD-IDF, SimCSE-WangchanBERTa with weighted by number of named entities as a vector representation, and using k-means as an clustering model.
Raku grammar classes for weather data entities. (Variable- and station names.)
This project was developed for a Kaggle competition focused on detecting Personally Identifiable Information (PII) in student writing. The primary objective was to build a robust model capable of identifying PII with high recall. The DeBERTa v3 transformer model was chosen for this task after comparing its performance with other transformer models.
Natural Language Processing
To explore the LLMs such as GPT-3. Demonstrate the strategies to design prompts are reproducible and produce a consistent result. Set up an MLOps pipeline that helps automate the task of using different LLMs and different topics. Allow improvements in the prompt design to be integrated without breaking the system and centralized log system shoul…
my documentation to learn more the basic concepts of NLP using Python
Character names scraping for Harry Potter series, name entity recognition on Harry Potter books and network analysis of characters
Natural Language Processing Course Labs
It's the third homework of Natural Language Processing course in Spring 2024 at Sharif University of Technology. It's about sentiment analysis in the level of texts. And also in the level of words, we do NER (Name Entity Recognition). In each part we designed a base model and a tranformer model. Also we collected a dataset for NER task.
Demo for Name Entity Recognition task with transformers
Name Entity Recognition with syntactic and Lexical Processing in identifying Disease and Treatment Entities in healthcare data
An AI-powered, but model-agnostic name-entity recognition toolkit.
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